tbuckley/cabot-search huggingface dataset:
Abstract 3011: Psychosocial stress, social support, and incident breast cancer by estrogen receptor status
Authors: Wayne R. Lawrence, Jasmine A. McDonald, Faustine Williams, Meredith S. Shiels, Neal D. Freedman, Ziqiang Lin, Jared W. Magnani
Journal: Cancer Research (2023)

Abstract Introduction: Chronic stress affects immune function and hormonal signaling and has been hypothesized to be associated with breast cancer, although results from the few prior studies are mixed and have not examined potential differences by estrogen receptor (ER) status. We investigated the relationship between stressful life events and incident breast cancer by ER status among postmenopausal women. We further evaluated whether this association is modified by social support. Methods: Our analysis included 76,951 postmenopausal women enrolled in the Women’s Health Initiative. Participants were followed for events for up to 26 years (median, 16.7). We generated Cox proportional hazards models adjusting for demographic, clinical, lifestyle/behavioral, and social factors to estimate hazard ratios (HR) and 95% confidence intervals (95%CI) for associations between baseline self-reported stressful life events and incident breast cancer by ER status. Results: The mean age was 63 (SD 7.3), and majority of participants were White race (83.5%) and married or in a marriage-like relationship (63.0%). In analyses stratified by ER status, there was no relationship between stressful life events and ER-positive breast cancer. In contrast, compared to women in the lowest quartile, those in higher quartiles had an increased risk of ER-negative breast cancer, where those in quartile 4 had the highest risk (Quartile 4 vs Quartile 1; HR=1.30; 95%CI 1.01-1.68; Ptrend=0.050). Additionally, associations were stronger for the highest versus lowest quartile of stressful life events among widowed women (HR=2.39; 95%CI 1.29-4.44; Pinteraction&amp;lt;0.001). Moreover, while the interaction was not statistically significant, we observed an elevated risk for ER-negative breast cancer among women in the highest quartile of stressful life events reporting lower social support. Conclusion: In this cohort of postmenopausal women, higher experiences of pre-diagnostic stressful life events were associated with increased risk of ER-negative breast cancer. Citation Format: Wayne R. Lawrence, Jasmine A. McDonald, Faustine Williams, Meredith S. Shiels, Neal D. Freedman, Ziqiang Lin, Jared W. Magnani. Psychosocial stress, social support, and incident breast cancer by estrogen receptor status [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3011.
Abstract 513: Suppressing YBX1 with a small molecule inhibitor sensitizes lung cancer to chemotherapy
Authors: Jee Min Lee, Sanjay Malhotra
Journal: Cancer Research (2023)

Abstract Y-box binding protein-1 (YBX1) is a multifunctional protein that regulates genes by binding to their DNA and/or RNA. YBX1 is known to contribute to tumorigenesis, disease progression and resistance by stabilizing the mRNAs of genes that are involved in cell growth and survival. High expression of YBX1 has been associated with poor clinical outcomes and decreased overall survival in over 20 cancer types including lung cancer. Given its critical roles in cancer progression and significant correlation with poor cancer prognosis, we hypothesized that inhibition of YBX1 may sensitize the cancer cells to chemotherapeutic agents. To profile the relationship between YBX1 and chemo-response, we aim to investigate the synergistic effect of YBX1 inhibition with standard of care chemotherapy drugs including cisplatin, in lung cancer using animal models and omics analysis. Our studies have found that SU056, a small molecule YBX1 inhibitor, effectively inhibits YBX1 expression in human lung adenocarcinoma cell lines and halts their cell proliferation and clonogenic potential, whereas overexpression of YBX1 promotes cell proliferation. SU056-mediated YBX1 inhibition also led to G2/M cell cycle arrest in these cells followed by apoptotic cell death. These results were further confirmed in A549 lung tumor xenograft model, where inhibitor treatment delayed tumor progression. We will present collective data from in vitro and in vivo studies with YBX1 inhibitor, combination studies with known chemotherapeutic drugs and omics analysis to suggest that targeting YBX1 has therapeutic potential to increase the efficacy of chemotherapeutic agents. Citation Format: Jee Min Lee, Sanjay Malhotra. Suppressing YBX1 with a small molecule inhibitor sensitizes lung cancer to chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 513.
Abstract 5209: Generation of humanized TREM2 mice for preclinical evaluation of therapeutics targeting tumor-associated macrophages
Authors: Ruili Lv, Jia Yu, Zhiyuan Shen, James Jin
Journal: Cancer Research (2023)

Abstract In recent years, immune checkpoint inhibitors (ICIs) have improved treatment for several types of cancer. However, the overall response rates remain low, and drug resistance can occur in patients after initial responses. This resistance is thought to be due in part to tumor-associated macrophages (TAMs), which can deploy a number of mechanisms to suppress anti-tumor immunity, resulting in tumor immune escape. Triggering receptor expressed on myeloid cells-2 (TREM2), a transmembrane receptor that is part of the immunoglobulin superfamily, is highly expressed on the surface of TAMs. Moreover, the expression level of TREM2 is reported to be higher in cancerous tissues vs normal tissues, with higher expression associated with poorer patient outcomes across multiple solid tumor types. Conversely, mice that lack Trem2 experience slower tumor cell expansion and are more responsive to anti-PD-1 immunotherapy, suggesting that TREM2 may be a promising therapeutic target for improving responses to ICI immunotherapies. To better assist the efficacy evaluation of anti TREM2 antibodies, Biocytogen has generated a humanized TREM2 mouse model. In these mice, exons 1-5 and the 3’UTR of mouse Trem2 gene, which encodes the full-length protein, were replaced by its human counterpart. In homozygous mice, human TREM2 protein expression is detected. The distribution of basal leukocyte subpopulations of blood, spleen, and lymph nodes in humanized B-hTREM2 mice were similar and comparable to those in wild-type C57BL/6 mice. Using the MC38 tumor model, we showed modest efficacy of an anti-human TREM2 antibody in inhibiting tumor growth in vivo. In summary, TREM2 humanized mice provide a powerful preclinical model for in vivo evaluation of anti-human TREM2 antibodies for cancer treatment. Citation Format: Ruili Lv, Jia Yu, Zhiyuan Shen, James Jin. Generation of humanized TREM2 mice for preclinical evaluation of therapeutics targeting tumor-associated macrophages. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5209.
Abstract 2669: RRM2 inhibition synergizes with PARP inhibitor in ovarian cancer cells
Authors: Chi-Wei Chen
Journal: Cancer Research (2023)

Abstract The use of Poly(ADP-ribose) polymerase inhibitors (PARPi) has been applied for the treatment of epithelial ovarian cancer (EOC) patients with homologous recombination (HR)-deficient disease, such as BRCA1/2 mutations. However, 40 - 70% of patients do not initially respond to PARPi in ovarian cancer with non-BRCA mutations, and acquired resistance has been demonstrated clinically. Ribonucleotide reductase (RNR, which consists of two RRM1 subunits and two RRM2 subunits) is the rate-limiting enzyme in the biosynthesis of dNTPs for DNA replication and DNA damage repair. As targeting RRM2 has been applied for many cancer therapies clinically or in clinical trials, this study hypothesizes that the inhibitory of RRM2 arguments PARPi potency against ovarian cancer. To this goal, this study evaluates the therapeutic effects of RRM2 inhibitor Osalmid (a resveratrol analog) alone or combined with PARPi by employing a panel of ovarian cancer cell lines using SRB assay. The effects on apoptosis and cell-cycle arrest were analyzed using flow cytometry. The cellular thermal shift assay was conducted to explore if Osalmid interacts with RRM2. DNA double strand break marker, ϒH2AX, was detected in compound-treated cells using Western blot. The results provide a new synthetic lethality by combining RRM2 inhibitor and PARPi. The combination induced apoptosis and caused the cell-cycle arrest. The results show that Osalmid binds to RRM2 directly. This study provides both a novel RRM2 inhibitor from a natural compound and a new understanding of the dNTP metabolic demands and may lead to urgently-needed strategies for the treatment of ovarian cancer. Citation Format: Chi-Wei Chen. RRM2 inhibition synergizes with PARP inhibitor in ovarian cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2669.
Abstract 6009: An end-to-end workflow for improved methylation detection
Authors: Lydia Bonar, Kristin Butcher, Michael Bocek, Holly Corbitt, Bryan Hoglund, Cibelle Nassif, Patrick Cherry, Derek Murphy, Jean Challacombe, Esteban Toro
Journal: Cancer Research (2023)

Abstract Introduction: DNA methylation at CpG nucleotide sites in eukaryotes is a key epigenetic mark that can help regulate gene expression. Specific changes in CpG methylation occur in many human cancers, making them a promising biomarker for early cancer detection. However, existing assays can be costly, lack specificity to regions of interest and often provide only semi-quantitative estimates of the methylation fraction. Here, we present a targeted methylome panel to decrease costs associated with next generation sequencing (NGS), methylation controls to calibrate quantitative assays and UMIs for accurate deduplication in low-diversity samples. Experimental Procedures: Genomic DNA (gDNA) was prepared for sequencing using the Twist Methylation Detection System consisting of enzymatic methylation conversion and hybrid capture using the Twist Human Methylome Panel. Twist’s synthetic CpG methylation level controls were spiked-in to gDNA and taken through the Twist Methylation Detection System to demonstrate their utility in calibrating methylation assays. Additionally, libraries were generated using cell free DNA (cfDNA) and either conventional or UMI-containing adapters to investigate the impact on quantitative detection and total unique coverage. Results: Using the Twist Human Methylome Panel at 150x raw coverage achieves uniform coverage with low off-bait Picard metrics. 6.59 million CpG sites were detected using a minimum depth of 10X. Target capture with the Human Methylome Panel allows for informative CpG calling of up to 82 samples on a single Illumina Novaseq S4 flowcell, compared to 3 samples per flowcell with a tradition whole-genome bisulfite sequencing (WGBS) assay. The Twist CpG methylation specific controls are constructed of 48 unique contrived sequences that contain a total of 8 different levels of methylation, ranging from 100% to 0%. Including these controls allows for quantitation of methylation levels in the experimental samples and qualification of the enzymatic conversion process. Conclusions: Our study leverages the Twist methylation detection portfolio to interrogate genome-wide methylation patterns for various applications. In combination, these tools can be used to design cost effective end-to-end assays. *For Research Use Only. Not for use in diagnostic procedures. Citation Format: Lydia Bonar, Kristin Butcher, Michael Bocek, Holly Corbitt, Bryan Hoglund, Cibelle Nassif, Patrick Cherry, Derek Murphy, Jean Challacombe, Esteban Toro. An end-to-end workflow for improved methylation detection. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6009.
Abstract 1920: Association of type 2 diabetes with overall and site-specific cancer among predominantly low-income Black and White Americans
Authors: Pranoti Pradhan, Wanqing Wen, Wei Zheng
Journal: Cancer Research (2023)

Abstract Background: Black individuals have the highest cancer incidence rate in the United States compared to White and other populations. Additionally, they are also twice as likely to develop type 2 diabetes (T2D) during their lifetime. Specifically considering the association between diabetes and cancer incidence, both diseases share common modifiable and nonmodifiable risk factors such as, aging, obesity, lack of physical activity, unhealthy diet, excessive alcohol consumption, and smoking. This association could vary between cancer types and race is also a factor which could disproportionately affect this relationship. However, studies on these associations are limited and the impact in terms of risk between the Black and White Americans remains unclear. Objective: We evaluated the association of T2D with risk of total and 26 site-specific cancers in a large cohort study conducted among predominantly low-income Black and White Americans who have a higher risk of T2D than other populations. Given the previous literature on T2D, cancer, and race, we hypothesized that T2D might be associated with varying risk for cancer. Methods: For this analysis, we used data from the Southern Community Cohort Study, an on-going prospective cohort study. Enrollment began in 2002 and concluded in 2009, with active follow-up until 2020. Study participants were asked whether they were diagnosed with T2D by physicians at the follow-up interviews and T2D was assessed as a time-varying covariate. Ascertainment of incident cancer was carried out via linkage to state cancer registries. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) after adjusting for confounders. Results: This study included 81,525 participants (30.2 % (N=24,581) White; 65.9% (N=53,711) Black) of whom 36.2% (N=29,487) were diagnosed with diabetes. Among those with diabetes, 10.3% (N=3,024) were diagnosed with cancer. Individuals with T2D had an elevated risk of several cancers, including pancreatic cancer (HR = 1.52, 95% CI: 1.18-1.96); but a reduced risk of esophageal cancer (HR = 0.55, 95% CI: 0.34-0.89). Some racial differences were found regarding the association of T2D with cancer risk. Conclusion: We found varying risks between T2D and risk of cancer between White and Black individuals. This highlights the importance of T2D prevention practices to help mitigate the risk of cancer and that further research is necessary to understand this association in minority individuals. Citation Format: Pranoti Pradhan, Wanqing Wen, Wei Zheng. Association of type 2 diabetes with overall and site-specific cancer among predominantly low-income Black and White Americans [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1920.
Abstract 3697: Modulation of Nicotinamide N-Methyl Transferase (NNMT) expression in TNBC is associated with altered cell membrane cholesterol levels
Authors: Woei-Yaw Chee, Deniz Nesli Dolcen, Lynda Bennett, Suzanne D. Conzen
Journal: Cancer Research (2023)

Abstract Introduction: Cancer cells often develop a program of metabolic adaptability and epigenetic remodelling to promote gene expression changes causing cancerous cell proliferation and invasion. The metabolic-epigenetic axis as a an underlying mechanism of carcinogenesis could lead to new approaches to cancer treatment. Nicotinamide N-Methyl Transferase (NNMT) is a metabolic enzyme that catalyzes the transfer of a methyl group from S-Adenosyl methionine (SAM) to nicotinamide, thereby decreasing the overall methyl pools for RNA, histone and DNA methylation. High NNMT expression has been previously associated with a poor outcome in TNBC. Hypothesis: We hypothesized that high versus low NNMT activity and resulting oncogenic gene expression in TNBC may alter histone methylation is association with altered metabolic pathway activation. Methods: We used two TNBC cell lines models, MDA-MB 231 and SUM159, to study the association between high versus low NNMT activity and metabolic pathway activation. Surprisingly, we identified gene expression pathways suggestive of increased free cholesterol biosynthesis gene expression with low NNMT, and then used biochemical assays and filipin to measure free cholesterol in both total high and low NNMT TNBC cell lysates and relative cholesterol expression in the High vs Low NNMT cell membranes. Results: In vitro, NNMT knockdown resulted in increased cholesterol synthetic pathway enzyme expression in both TNBC cell lines. In vivo studies also showed that protein expression of cholesterol synthesis enzymes was also increased in NNMT-depleted MDA-MB 231 xenografted tumors. Interestingly, the increased cholesterol level upon NNMT depletion was mainly localized at the plasma membrane via Filipin III staining, suggesting a possible association with less cellular membrane fluidity and a decreased oncogenic phenotype. In ongoing experiments, we are evaluating whether changes in histone methylation in high vs low NNMT cells are associated with differential expression of cholesterol biosynthetic genes, and free cholesterol production. We hypothesize that NNMT might epigenetically decrease the methylation of histones regulating cholesterol production in TNBC. Citation Format: Woei-Yaw Chee, Deniz Nesli Dolcen, Lynda Bennett, Suzanne D. Conzen. Modulation of Nicotinamide N-Methyl Transferase (NNMT) expression in TNBC is associated with altered cell membrane cholesterol levels. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3697.
Abstract 6697: Deep learning algorithm for cancer detection using multimodal characteristics of whole methylome sequencing of cf-DNA
Authors: Juntae Park, Minjung Kim, Sook Ryun Park, Ki-Byung Song, Eunsung Jun, Dongryul Oh, Jeong-Won Lee, Young Sik Park, Ki-Won Song, Jeong-Sik Byeon, Bo Hyun Kim, Chang-Seok Ki, Eunhae Cho
Journal: Cancer Research (2023)

Abstract Background Various cell-free DNA (cfDNA) features including methylation and genomic profiles have been investigated for their potential use in early cancer detection. We developed deep learning models based the data generated by the enzymatic conversion based whole methylome sequencing of cfDNA. Methods Cell-free whole genome Enzymatic Methyl sequencing(cfWEMseq) data were generated from 198 cancer patients (stage I: 11%, II: 17%, III: 22%, IV: 20%, unknown: 31%) and 69 healthy controls. The cancer types were consisted of breast (n=31), liver (n=24), esophageal (n=38), pancreatic (n=30), colon (n=34), ovarian (n=18), and lung (n=23). Sequence data was produced on average of 200 million reads using Novaseq 6000 (Illumina). For model training and evaluation, data partitioning was stratified by cancer type, and 5-fold cross validation was used. Coverage and methylation beta values ​​were calculated by binning at fixed size of 100K, 1M, and 5M base and variable size from Topologically Associated Domains (TAD). Genome Coverage (GC), Genome Methylation Beta values ​​(GMB), and Mutation Signature (MS) features were trained using a one-dimensional convolutional neural network (1D-CNN). The performance of the model was evaluated by measuring the average value of the results measured in each test set of 5 fold. Results We tested the cancer detection performance of various feature combinations using all data from cfWEMseq (n=267). Regardless of the bin size, the GMB single model achieved higher performance than the GC single model. The best-performing model is the ensemble model of GMB (100k bin) and MS. The cancer detection performance of this ensemble model reached an accuracy 96% (CI: 93.6% to 98.1%), AUC 0.99 (CI: 0.97 to 1.0) and sensitivity 98.0% (CI: 92.4% to 99.5%) with a specificity of 90%. Conclusions These results provide an opportunity for higher accuracies by integrating methylation information and genomic data using cfWEMseq. This research was supported through the National Research Foundation (NRF) funded by the Ministry of Science and ICT (2020M2D9A3094213). Citation Format: Juntae Park, Minjung Kim, Sook Ryun Park, Ki-Byung Song, Eunsung Jun, Dongryul Oh, Jeong-Won Lee, Young Sik Park, Ki-Won Song, Jeong-Sik Byeon, Bo Hyun Kim, Chang-Seok Ki, Eunhae Cho. Deep learning algorithm for cancer detection using multimodal characteristics of whole methylome sequencing of cf-DNA. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6697.